Lecture 17: Dashboards in R Part 1

Dashboards in R

• I’ll tell you up front that doing a dashboard in R is going to be more work than doing one in Tableau
• So why bother?
• As always, there is more flexibility and control in R
• Plus you get a lot more control over cleaning and manipulating the underlying data
• Plus, for doing the basic stuff it’s not too hard
• And it’s easier to host online without access to paid Tableau versions

Plus…

• R dashboard provide you access to a huge range of free premade graphical tools through htmlwidgets
• See the htmlwidgets gallery
• Good for everything, but the maps do really stand out
• As you learn these tools you can include them not just in dashboards but in RMarkdown docs, too
• And also, it’s not just htmlwidgets, but also Shiny!

Shiny

• Shiny is more internal to R than htmlwidgets; it’s an R-specific interactive site builder
• You can add user controls to allow them to change parameters around and see the results
• Great for dashboards, but even beyond that, learning Shiny for the purpose of dashboards will also give you the tools to create data-based apps that you can upload on their own
• Super cool stuff you can do if you branch out with this!

On to dashboards

• There are two approaches to doing dashboards in R. One is Shiny-focused, and the other is htmlwidgets focused (but lets you include Shiny stuff)
• We’ll be going with the latter, as it’s easier, but the Shiny one is good too.
• Our approach will be to use flexdashboard
• Install flexdashboard with install.packages('flexdashboard')
• Then you can open a new Flex Dashboard by going File \(\rightarrow\) New File \(\rightarrow\) R Markdown \(\rightarrow\) From Template \(\rightarrow\) Flex Dashboard.
• Plenty of info on https://rmarkdown.rstudio.com/flexdashboard/

What Do We See?

What Do We See?

• Up top is the YAML - we know this from working with RMarkdown docs. Basic info about the doc itself
• Then we have our layout structure - columns of stuff, with multiple graphs per columns
• We can if we prefer lay things out row-wise (see next slide), and “storyboard” where it goes from page to page like a flipbook
• Then within each column we have our graphs, labeled with three hashes

Row orientation

Content

• We can of course fill those charts with regular-ol ggplot2 output
• (or gganimate, or whatever else produces an image file)
• Or Shiny stuff, which we’ll get to next time
• But also there are a bunch of packages designed to output htmlwidgets that we can include! Many of them look real nice
• Let’s peruse: The HTMLWidgets R Gallery

Plotly

• One of the easiest to use, and one you’ll reach for very frequently, is plotly
• Plotly is a JavaScript-based graphing tool with built-in interactivity
• And, super conveniently, you don’t really need to do anything new to use it
• Just toss your regular ggplot2 object into ggplotly from the plotly package and you’re ready to go
• (ggiraf is a similar ggplot2-but-interactive approach but with a little more control and flexibility, but requires changing the original ggplot2 code to add interactive elements. Up to you!)

Plotly

p <- mtcars %>% 
  mutate(CarName = row.names(mtcars),
    Transmission = factor(am, labels = c('Automatic','Manual'))) %>%
  ggplot(aes(x = mpg, y = hp, color = Transmission, label = CarName)) + 
  theme_minimal() + 
  theme(text = element_text(size = 15, family = 'serif')) + 
  geom_point() +
  labs(x = 'Miles per Gallon', y = "Horsepower",
       title = 'Miles per Gallon vs. Horsepower by Transmission Type') +
  guides(color = FALSE) + 
  scale_color_paletteer_d('yarrr::google') + 
  annotate(geom = 'label', x = 20, y = 300, label = 'Notice the Downward Trend')

Plotly

• Most things will translate right over; for others we might need to look a little bit into the plotly docs - notice how the legend will still show up despite us turning it off, and the annotation doesn’t carry over.
• Let’s take a look on the next slide

Plotly

plotly::ggplotly(p)

Material

• When it comes to any complex programmatic system like this, you always want to look at the docs at least a little
• Let’s just spend some time walking through https://rmarkdown.rstudio.com/flexdashboard/ if only to see what’s possible and what we need to know
• Then come back here

Practice

• Open a Flex Dashboard template
• Load any data set (in the Dashboard code)
• Make a Flex Dashboard and set its theme
• Include three graphs: one regular ggplot2, that same graph again in plotly version with ggplotly, and one graph other than a Plotly from http://gallery.htmlwidgets.org/
• If you can make it look nice that’s great! But focus more on getting it to work.